Pub Date : 2022-03-28DOI: 10.15849/ijasca.220328.10
Sajidah Mahmood
Abstract COVID-19 pandemic enforced students in schools and universities all around the world to study using the online and blinded learning. In these learning models, students depend on the Internet for information searching of different scientific essentials to improve their skills and to overcome the gap of facing instructors. One of the most popular sources of information is Wikipedia. In this work, we attempt to study the relations of different math essential pages of Wikipedia to find the relation between these topics. A graph has been constructed for these pages. The graph theoretical metrics, such as, centrality, edge weights and clustering coefficient have been extracted of the constructed graph. The extracted values have been investigated to gain more insights of the math topics that should be studied first. The extracted results show that the in-degree property of the articles and the betweenness value of these articles are correlated. Moreover, there is no relation between the in /out-degree of the pages. Finally, the constructed graph has a small average shortest path and a high global cluster coefficient. This proves that the constructed graph follows the small world phenomenon. Keywords: Graph metrics, Math essentials, Gephi, Small world phenomenon, Directed graph
{"title":"Studying the Wikipedia Math Essential Pages using Graph Theory Metrics","authors":"Sajidah Mahmood","doi":"10.15849/ijasca.220328.10","DOIUrl":"https://doi.org/10.15849/ijasca.220328.10","url":null,"abstract":"Abstract COVID-19 pandemic enforced students in schools and universities all around the world to study using the online and blinded learning. In these learning models, students depend on the Internet for information searching of different scientific essentials to improve their skills and to overcome the gap of facing instructors. One of the most popular sources of information is Wikipedia. In this work, we attempt to study the relations of different math essential pages of Wikipedia to find the relation between these topics. A graph has been constructed for these pages. The graph theoretical metrics, such as, centrality, edge weights and clustering coefficient have been extracted of the constructed graph. The extracted values have been investigated to gain more insights of the math topics that should be studied first. The extracted results show that the in-degree property of the articles and the betweenness value of these articles are correlated. Moreover, there is no relation between the in /out-degree of the pages. Finally, the constructed graph has a small average shortest path and a high global cluster coefficient. This proves that the constructed graph follows the small world phenomenon. Keywords: Graph metrics, Math essentials, Gephi, Small world phenomenon, Directed graph","PeriodicalId":38638,"journal":{"name":"International Journal of Advances in Soft Computing and its Applications","volume":"100 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41306831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-28DOI: 10.15849/ijasca.220328.14
M. Khan, Muhammad Rajwana
Abstract Aerial scene classification, which aims to automatically tag an aerial image with a specific semantic category, is a fundamental problem for understanding high-resolution remote sensing imagery. The classification of remote sensing image scenes can provide significant value, from forest fire monitoring to land use and land cover classification. From the first aerial photographs of the early 20th century to today's satellite imagery, the amount of remote sensing data has increased geometrically with higher resolution. The need to analyze this modern digital data has motivated research to accelerate the classification of remotely sensed images. Fortunately, the computer vision community has made great strides in classifying natural images. Transformers first applied to the field of natural language processing, is a type of deep neural network mainly based on the self-attention mechanism. Thanks to its strong representation capabilities, researchers are looking at ways to apply transformers to computer vision tasks. In a variety of visual benchmarks, transformer-based models perform similar to or better than other types of networks such as convolutional and recurrent networks. Given its high performance and less need for vision-specific inductive bias, the transformer is receiving more and more attention from the computer vision community. In this paper, we provide a systematic review of the Transfer Learning and Transformer techniques for scene classification using AID datasets. Both approaches give an accuracy of 80% and 84%, for the AID dataset. Keywords: remote sensing, vision transformers, transfer learning, classification accuracy
{"title":"Remote Sensing Image Classification Via Vision Transformer and Transfer Learning","authors":"M. Khan, Muhammad Rajwana","doi":"10.15849/ijasca.220328.14","DOIUrl":"https://doi.org/10.15849/ijasca.220328.14","url":null,"abstract":"Abstract Aerial scene classification, which aims to automatically tag an aerial image with a specific semantic category, is a fundamental problem for understanding high-resolution remote sensing imagery. The classification of remote sensing image scenes can provide significant value, from forest fire monitoring to land use and land cover classification. From the first aerial photographs of the early 20th century to today's satellite imagery, the amount of remote sensing data has increased geometrically with higher resolution. The need to analyze this modern digital data has motivated research to accelerate the classification of remotely sensed images. Fortunately, the computer vision community has made great strides in classifying natural images. Transformers first applied to the field of natural language processing, is a type of deep neural network mainly based on the self-attention mechanism. Thanks to its strong representation capabilities, researchers are looking at ways to apply transformers to computer vision tasks. In a variety of visual benchmarks, transformer-based models perform similar to or better than other types of networks such as convolutional and recurrent networks. Given its high performance and less need for vision-specific inductive bias, the transformer is receiving more and more attention from the computer vision community. In this paper, we provide a systematic review of the Transfer Learning and Transformer techniques for scene classification using AID datasets. Both approaches give an accuracy of 80% and 84%, for the AID dataset. Keywords: remote sensing, vision transformers, transfer learning, classification accuracy","PeriodicalId":38638,"journal":{"name":"International Journal of Advances in Soft Computing and its Applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49077621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-28DOI: 10.15849/ijasca.211128.08
A. Salma, A. Bustamam, A. Yudantha, A. Victor, W. Mangunwardoyo
The number of people around the world who have diabetes is about 422 million. Diabetes seriously affects the blood vessels in the retina, a disease called diabetic retinopathy (DR). The ophthalmologist examines signs through fundus images, such microaneurysm, exudates and neovascularisation and determines the suitable treatment for patient based on the condition. Currently, doctors require a long time and professional skills to detect DR. This study aimed to implement artificial intelligence (AI) to resolve the lack of current methods. This study implemented AI for detecting and classifying DR. AI uses deep learning, such the attention mechanism algorithm and AlexNet architecture. The attention mechanism algorithm focuses on detecting the pathological area in the fundus images, and AlexNet is used to classify DR into five levels based on the pathological area. This study also compared AlexNet architecture with and without attention mechanism. We obtained 344 fundus images from the Kaggle dataset, which contains normal, mild, moderate, severe and proliferative DR. The highest accuracy in this study is up to 91% and used the attention mechanism algorithm and AlexNet architecture. The experiment shows that our proposed method can provide results that can detect the pathological areas and effectively classify DR. Keywords: Artificial intelligence, Diabetic Retinopathy, Attention Mechanism, AlexNet
{"title":"Artificial Intelligence Approach in Multiclass Diabetic Retinopathy Detection Using Convolutional Neural Network and Attention Mechanism","authors":"A. Salma, A. Bustamam, A. Yudantha, A. Victor, W. Mangunwardoyo","doi":"10.15849/ijasca.211128.08","DOIUrl":"https://doi.org/10.15849/ijasca.211128.08","url":null,"abstract":"The number of people around the world who have diabetes is about 422 million. Diabetes seriously affects the blood vessels in the retina, a disease called diabetic retinopathy (DR). The ophthalmologist examines signs through fundus images, such microaneurysm, exudates and neovascularisation and determines the suitable treatment for patient based on the condition. Currently, doctors require a long time and professional skills to detect DR. This study aimed to implement artificial intelligence (AI) to resolve the lack of current methods. This study implemented AI for detecting and classifying DR. AI uses deep learning, such the attention mechanism algorithm and AlexNet architecture. The attention mechanism algorithm focuses on detecting the pathological area in the fundus images, and AlexNet is used to classify DR into five levels based on the pathological area. This study also compared AlexNet architecture with and without attention mechanism. We obtained 344 fundus images from the Kaggle dataset, which contains normal, mild, moderate, severe and proliferative DR. The highest accuracy in this study is up to 91% and used the attention mechanism algorithm and AlexNet architecture. The experiment shows that our proposed method can provide results that can detect the pathological areas and effectively classify DR. Keywords: Artificial intelligence, Diabetic Retinopathy, Attention Mechanism, AlexNet","PeriodicalId":38638,"journal":{"name":"International Journal of Advances in Soft Computing and its Applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45137348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-28DOI: 10.15849/ijasca.211128.07
Khalid Jaber, Mohammad Abduljawad, Amal Ahmad, Mohammad Abdallah4, Mousa Salah, N. Alhindawi
The E-learning standard is made up of several different quality elements and characteristics. Scholars examined the effectiveness of E-learning from a variety of perspectives. However, studies concerning the quality of E-learning mobile applications in particular are limited. Hence, the present study looks at the factors that influence the use of the E-learning mobile application by students and instructors of the Al-Zaytoonah University in Jordan throughout the academic year 2020–2021. The research instrument was initially validated. Subsequently, several quality factors were adopted to anticipate the factors affecting the adoption of the Elearning.ZUJ mobile application of nine hundred thirty-one students and one hundred nine instructors in this study. Regarding the actual usage of the E-learning mobile application for academic activities, in different proportions, the findings of this investigation were compatible with the adopted quality factors. Results revealed a significant positive relationship between the perceived reliability and demand for using E-learning applications. In addition to a significant positive relationship between the perceived benefit and behavioral intention to use E-learning mobile applications, the results show the following perceived quality factors: reliability, efficiency, integrity, usability, satisfaction, and supportability. The findings should be valuable to educational officials at the Al-Zaytoonah University of Jordan and elsewhere as existing technology could be improved or they could embrace new technology for academic purposes. Keywords: E-learning mobile applications, E-learning quality, quality, quality factors.
{"title":"E-learning Mobile Application Evaluation: Al-Zaytoonah University as a Case Study","authors":"Khalid Jaber, Mohammad Abduljawad, Amal Ahmad, Mohammad Abdallah4, Mousa Salah, N. Alhindawi","doi":"10.15849/ijasca.211128.07","DOIUrl":"https://doi.org/10.15849/ijasca.211128.07","url":null,"abstract":"The E-learning standard is made up of several different quality elements and characteristics. Scholars examined the effectiveness of E-learning from a variety of perspectives. However, studies concerning the quality of E-learning mobile applications in particular are limited. Hence, the present study looks at the factors that influence the use of the E-learning mobile application by students and instructors of the Al-Zaytoonah University in Jordan throughout the academic year 2020–2021. The research instrument was initially validated. Subsequently, several quality factors were adopted to anticipate the factors affecting the adoption of the Elearning.ZUJ mobile application of nine hundred thirty-one students and one hundred nine instructors in this study. Regarding the actual usage of the E-learning mobile application for academic activities, in different proportions, the findings of this investigation were compatible with the adopted quality factors. Results revealed a significant positive relationship between the perceived reliability and demand for using E-learning applications. In addition to a significant positive relationship between the perceived benefit and behavioral intention to use E-learning mobile applications, the results show the following perceived quality factors: reliability, efficiency, integrity, usability, satisfaction, and supportability. The findings should be valuable to educational officials at the Al-Zaytoonah University of Jordan and elsewhere as existing technology could be improved or they could embrace new technology for academic purposes. Keywords: E-learning mobile applications, E-learning quality, quality, quality factors.","PeriodicalId":38638,"journal":{"name":"International Journal of Advances in Soft Computing and its Applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48074840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-28DOI: 10.15849/ijasca.211128.02
Yahya Hussein, Ali Mohammed Sahan
The human ear has unique and attractive details; therefore, human ear recognition is one of the most important fields in the biometric domains. In this work, we proposed an efficient and intelligent ear recognition technique based on particle swarm optimization, discrete wavelet transform, and fuzzy neural network. Discrete wavelet transform is used to provide comprise and effective features about the ear image, while the particle swarm optimization utilized to select more effective and attractive features. Furthermore, using particle swarm optimization leads to reduce the complexity of the classification stage since it reduces the number of the features. Fuzzy neural network used in the classification stage in order to provide strong distinguishing between the testing and training ear images. many experiments performed using two ear databases to examine the accuracy of the proposed technique. The analysis of the results refers that the presented technique gained high recognition accuracy using various data sets with less complexity. Keywords: Ear recognition; bio-metric; discrete wavelet transform, particle swarm optimization, fuzzy neural network.
{"title":"An Intelligent Ear Recognition Technique","authors":"Yahya Hussein, Ali Mohammed Sahan","doi":"10.15849/ijasca.211128.02","DOIUrl":"https://doi.org/10.15849/ijasca.211128.02","url":null,"abstract":"The human ear has unique and attractive details; therefore, human ear recognition is one of the most important fields in the biometric domains. In this work, we proposed an efficient and intelligent ear recognition technique based on particle swarm optimization, discrete wavelet transform, and fuzzy neural network. Discrete wavelet transform is used to provide comprise and effective features about the ear image, while the particle swarm optimization utilized to select more effective and attractive features. Furthermore, using particle swarm optimization leads to reduce the complexity of the classification stage since it reduces the number of the features. Fuzzy neural network used in the classification stage in order to provide strong distinguishing between the testing and training ear images. many experiments performed using two ear databases to examine the accuracy of the proposed technique. The analysis of the results refers that the presented technique gained high recognition accuracy using various data sets with less complexity. Keywords: Ear recognition; bio-metric; discrete wavelet transform, particle swarm optimization, fuzzy neural network.","PeriodicalId":38638,"journal":{"name":"International Journal of Advances in Soft Computing and its Applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44018291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-28DOI: 10.15849/ijasca.211128.16
Mosa Salah, Ahmad A. Mazhar, M. Mizher
Cloud computing is a model of technology that offers access to system resources with advanced level of services ability. These resources are measured reliable, flexible and affordable for several kinds of applications and users. Gaming manufacturing is one filed that expands the profits of cloud computing as numerous new cloud gaming designs have been presented. Many advantages of cloud gaming have exaggerated the success of gaming based on the improvements on traditional online gaming. Though, cloud gaming grieves from several downsides such as the massive amount of needed video processing and the computational complexity required for that. This paper displays the original system drawbacks and develops a new and original algorithm to speed up the encoding process by reduces the computational complexity by exploiting the block type and location. Enhancements on the video codec led to 12.2% speeding up on the over-all encoding time with slight loss of users’ satisfactions. Keywords: Cloud gaming, Computational complexity, Motion estimation, HEVC, Video Encoding
{"title":"Optimization of Video Cloud Gaming Using Fast HEVC Video Compression Technique","authors":"Mosa Salah, Ahmad A. Mazhar, M. Mizher","doi":"10.15849/ijasca.211128.16","DOIUrl":"https://doi.org/10.15849/ijasca.211128.16","url":null,"abstract":"Cloud computing is a model of technology that offers access to system resources with advanced level of services ability. These resources are measured reliable, flexible and affordable for several kinds of applications and users. Gaming manufacturing is one filed that expands the profits of cloud computing as numerous new cloud gaming designs have been presented. Many advantages of cloud gaming have exaggerated the success of gaming based on the improvements on traditional online gaming. Though, cloud gaming grieves from several downsides such as the massive amount of needed video processing and the computational complexity required for that. This paper displays the original system drawbacks and develops a new and original algorithm to speed up the encoding process by reduces the computational complexity by exploiting the block type and location. Enhancements on the video codec led to 12.2% speeding up on the over-all encoding time with slight loss of users’ satisfactions. Keywords: Cloud gaming, Computational complexity, Motion estimation, HEVC, Video Encoding","PeriodicalId":38638,"journal":{"name":"International Journal of Advances in Soft Computing and its Applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48036754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-28DOI: 10.15849/ijasca.211128.13
Abdullah A. Alabdulatif
Many different networks that rely on short-distance wireless technology for their functions utilize the IEEE 802.15.4 Standard, especially in the case of systems that experience a low level of traffic. The networks using this standard are typically based on the Low-Rate Wireless Personal Area Network, herein called the LR-WPAN; this network is used for the provision of both the physical layer, herein referred to as the PHY, and the media access control, herein abbreviated as the MAC. There are four security features in the IEEE 802.15.4 Standard that are designed to ensure the safe and secure transmission of data through the network. Disconnection from the network is managed and controlled by the message authentication code, herein referred to as the MAC, while the coordinator personal area network, herein abbreviated as the PAN, is also able to trigger the disconnection. However, the process of disconnection from the network is one area of vulnerability to denial-of-service attacks, herein referred to as DoS; this highlights a major shortcoming of the IEEE 802.15.4 Standard’s security features. This paper is intended to contribute to the improvement of security for the IEEE network by conducting a specific and in-depth review of available literature as well as conducting an analysis of the disassociation process. In doing so, potential new threats will be highlighted, and this data can be used to improve the security of the IEEE 802.15.4 Standard. Overall, in this paper, the role of the Castalia tool in the OMNET++ environment is analysed and interpreted for these potential new threats. Also, this paper proposes a solution to such threats to improve the security IEEE 802.15.4 disassociation process. Keywords: Disassociation vulnerability of IEEE 802.15.4 Standard, DoS attack, IoT security.
{"title":"Potential Security Vulnerabilities of the IEEE 802.15.4 Standard and a Proposed Solution Against the Dissociation Process","authors":"Abdullah A. Alabdulatif","doi":"10.15849/ijasca.211128.13","DOIUrl":"https://doi.org/10.15849/ijasca.211128.13","url":null,"abstract":"Many different networks that rely on short-distance wireless technology for their functions utilize the IEEE 802.15.4 Standard, especially in the case of systems that experience a low level of traffic. The networks using this standard are typically based on the Low-Rate Wireless Personal Area Network, herein called the LR-WPAN; this network is used for the provision of both the physical layer, herein referred to as the PHY, and the media access control, herein abbreviated as the MAC. There are four security features in the IEEE 802.15.4 Standard that are designed to ensure the safe and secure transmission of data through the network. Disconnection from the network is managed and controlled by the message authentication code, herein referred to as the MAC, while the coordinator personal area network, herein abbreviated as the PAN, is also able to trigger the disconnection. However, the process of disconnection from the network is one area of vulnerability to denial-of-service attacks, herein referred to as DoS; this highlights a major shortcoming of the IEEE 802.15.4 Standard’s security features. This paper is intended to contribute to the improvement of security for the IEEE network by conducting a specific and in-depth review of available literature as well as conducting an analysis of the disassociation process. In doing so, potential new threats will be highlighted, and this data can be used to improve the security of the IEEE 802.15.4 Standard. Overall, in this paper, the role of the Castalia tool in the OMNET++ environment is analysed and interpreted for these potential new threats. Also, this paper proposes a solution to such threats to improve the security IEEE 802.15.4 disassociation process. Keywords: Disassociation vulnerability of IEEE 802.15.4 Standard, DoS attack, IoT security.","PeriodicalId":38638,"journal":{"name":"International Journal of Advances in Soft Computing and its Applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41853440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-28DOI: 10.15849/ijasca.211128.12
Sokyna M. Alqatawneh, Khalid Jaber, Mosa Salah, D. Yehia, Omayma Alqatawneh, Abdulrahman Abulahoum
Like many countries, Jordan has resorted to lockdown in an attempt to contain the outbreak of Coronavirus (Covid-19). A set of precautions such as quarantines, isolations, and social distancing were taken in order to tackle its rapid spread of Covid-19. However, the authorities were facing a serious issue with enforcing quarantine instructions and social distancing among its people. In this paper, a social distancing mentoring system has been designed to alert the authorities if any of the citizens violated the quarantine instructions and to detect the crowds and measure their social distancing using an object tracking technique that works in real-time base. This system utilises the widespread surveillance cameras that already exist in public places and outside many residential buildings. To ensure the effectiveness of this approach, the system uses cameras deployed on the campus of Al-Zaytoonah University of Jordan. The results showed the efficiency of this system in tracking people and determining the distances between them in accordance with public safety instructions. This work is the first approach to handle the classification challenges for moving objects using a shared-memory model of multicore techniques. Keywords: Covid-19, Parallel computing, Risk management, Social distancing, Tracking system.
{"title":"Employing of Object Tracking System in Public Surveillance Cameras to Enforce Quarantine and Social Distancing Using Parallel Machine Learning Techniques","authors":"Sokyna M. Alqatawneh, Khalid Jaber, Mosa Salah, D. Yehia, Omayma Alqatawneh, Abdulrahman Abulahoum","doi":"10.15849/ijasca.211128.12","DOIUrl":"https://doi.org/10.15849/ijasca.211128.12","url":null,"abstract":"Like many countries, Jordan has resorted to lockdown in an attempt to contain the outbreak of Coronavirus (Covid-19). A set of precautions such as quarantines, isolations, and social distancing were taken in order to tackle its rapid spread of Covid-19. However, the authorities were facing a serious issue with enforcing quarantine instructions and social distancing among its people. In this paper, a social distancing mentoring system has been designed to alert the authorities if any of the citizens violated the quarantine instructions and to detect the crowds and measure their social distancing using an object tracking technique that works in real-time base. This system utilises the widespread surveillance cameras that already exist in public places and outside many residential buildings. To ensure the effectiveness of this approach, the system uses cameras deployed on the campus of Al-Zaytoonah University of Jordan. The results showed the efficiency of this system in tracking people and determining the distances between them in accordance with public safety instructions. This work is the first approach to handle the classification challenges for moving objects using a shared-memory model of multicore techniques. Keywords: Covid-19, Parallel computing, Risk management, Social distancing, Tracking system.","PeriodicalId":38638,"journal":{"name":"International Journal of Advances in Soft Computing and its Applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49663631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-28DOI: 10.15849/ijasca.211128.11
M. Khder
Web scraping or web crawling refers to the procedure of automatic extraction of data from websites using software. It is a process that is particularly important in fields such as Business Intelligence in the modern age. Web scrapping is a technology that allow us to extract structured data from text such as HTML. Web scrapping is extremely useful in situations where data isn’t provided in machine readable format such as JSON or XML. The use of web scrapping to gather data allows us to gather prices in near real time from retail store sites and provide further details, web scrapping can also be used to gather intelligence of illicit businesses such as drug marketplaces in the darknet to provide law enforcement and researchers valuable data such as drug prices and varieties that would be unavailable with conventional methods. It has been found that using a web scraping program would yield data that is far more thorough, accurate, and consistent than manual entry. Based on the result it has been concluded that Web scraping is a highly useful tool in the information age, and an essential one in the modern fields. Multiple technologies are required to implement web scrapping properly such as spidering and pattern matching which are discussed. This paper is looking into what web scraping is, how it works, web scraping stages, technologies, how it relates to Business Intelligence, artificial intelligence, data science, big data, cyber securityو how it can be done with the Python language, some of the main benefits of web scraping, and what the future of web scraping may look like, and a special degree of emphasis is placed on highlighting the ethical and legal issues. Keywords: Web Scraping, Web Crawling, Python Language, Business Intelligence, Data Science, Artificial Intelligence, Big Data, Cloud Computing, Cybersecurity, legal, ethical.
{"title":"Web Scraping or Web Crawling: State of Art, Techniques, Approaches and Application","authors":"M. Khder","doi":"10.15849/ijasca.211128.11","DOIUrl":"https://doi.org/10.15849/ijasca.211128.11","url":null,"abstract":"Web scraping or web crawling refers to the procedure of automatic extraction of data from websites using software. It is a process that is particularly important in fields such as Business Intelligence in the modern age. Web scrapping is a technology that allow us to extract structured data from text such as HTML. Web scrapping is extremely useful in situations where data isn’t provided in machine readable format such as JSON or XML. The use of web scrapping to gather data allows us to gather prices in near real time from retail store sites and provide further details, web scrapping can also be used to gather intelligence of illicit businesses such as drug marketplaces in the darknet to provide law enforcement and researchers valuable data such as drug prices and varieties that would be unavailable with conventional methods. It has been found that using a web scraping program would yield data that is far more thorough, accurate, and consistent than manual entry. Based on the result it has been concluded that Web scraping is a highly useful tool in the information age, and an essential one in the modern fields. Multiple technologies are required to implement web scrapping properly such as spidering and pattern matching which are discussed. This paper is looking into what web scraping is, how it works, web scraping stages, technologies, how it relates to Business Intelligence, artificial intelligence, data science, big data, cyber securityو how it can be done with the Python language, some of the main benefits of web scraping, and what the future of web scraping may look like, and a special degree of emphasis is placed on highlighting the ethical and legal issues. Keywords: Web Scraping, Web Crawling, Python Language, Business Intelligence, Data Science, Artificial Intelligence, Big Data, Cloud Computing, Cybersecurity, legal, ethical.","PeriodicalId":38638,"journal":{"name":"International Journal of Advances in Soft Computing and its Applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45346353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-28DOI: 10.15849/ijasca.211128.06
Yakobus Wiciaputra, J. Young, A. Rusli
With the large amount of text information circulating on the internet, there is a need of a solution that can help processing data in the form of text for various purposes. In Indonesia, text information circulating on the internet generally uses 2 languages, English and Indonesian. This research focuses in building a model that is able to classify text in more than one language, or also commonly known as multilingual text classification. The multilingual text classification will use the XLM-RoBERTa model in its implementation. This study applied the transfer learning concept used by XLM-RoBERTa to build a classification model for texts in Indonesian using only the English News Dataset as a training dataset with Matthew Correlation Coefficient value of 42.2%. The results of this study also have the highest accuracy value when tested on a large English News Dataset (37,886) with Matthew Correlation Coefficient value of 90.8%, accuracy of 93.3%, precision of 93.4%, recall of 93.3%, and F1 of 93.3% and the accuracy value when tested on a large Indonesian News Dataset (70,304) with Matthew Correlation Coefficient value of 86.4%, accuracy, precision, recall, and F1 values of 90.2% using the large size Mixed News Dataset (108,190) in the model training process. Keywords: Multilingual Text Classification, Natural Language Processing, News Dataset, Transfer Learning, XLM-RoBERTa
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